The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which inclu...
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BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is r...
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience lab.ratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the availab.e imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
Network-on-Chip architectures are scalab.e on-chip interconnection networks. They replace the inefficient shared buses and are suitable for multicore and manycore systems. This paper presents an Optimized Simulated An...
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ISBN:
(纸本)9783642325472
Network-on-Chip architectures are scalab.e on-chip interconnection networks. They replace the inefficient shared buses and are suitable for multicore and manycore systems. This paper presents an Optimized Simulated Annealing (OSA) algorithm for the Network-on-Chip application mapping problem. With OSA, the cores are implicitly and dynamically clustered using knowledge about communication demands. We show that OSA is a more feasible Simulated Annealing approach to NoC application mapping by comparing it with a general Simulated Annealing algorithm and a Branch and Bound algorithm, too. Using real applications we show that OSA is significantly faster than a general Simulated Annealing, without giving worse solutions. OSA proves to be feasible for Networks-on-Chip with more than 100 nodes. Also, compared to a Branch and Bound technique, it gives better solutions, as the problem size increases, while in terms of speed and memory consumption the two algorithms are comparable.
In today's computerarchitectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) tec...
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ISBN:
(纸本)9783642325472
In today's computerarchitectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) techniques. We developed the Framework for Automatic Design Space Exploration (FADSE) which is focused on microarchitectural optimizations. This framework includes several state-of-the art heuristic algorithms. In this paper we selected three of them, NSGA-II and SPEA2 as genetic algorithms as well as SMPSO as a particle swarm optimization, and compared their performance. As test case we optimize the parameters of the Grid ALU Processor (GAP) microarchitecture and then GAP together with the post-link code optimizer GAPtimize. An analysis of the simulation results shows a very good performance of all the three algorithms. SMPSO reveals the fastest convergence speed. A clear winner between NSGA-II and SPEA2 cannot be determined.
This paper presents a preliminary PhD research towards developing a framework to evaluate and optimize application mapping algorithms for Network-on-Chip architectures. Several such algorithms have been proposed for m...
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ISBN:
(纸本)9781424473359
This paper presents a preliminary PhD research towards developing a framework to evaluate and optimize application mapping algorithms for Network-on-Chip architectures. Several such algorithms have been proposed for mapping the threads of a parallel application on a NoC architecture. However, the performance of those algorithms is evaluated only on some specific NoC designs. A unified approach for evaluating such algorithms allows a better comparison of their performance and can potentially lead to some optimizations. The proposed framework is intended to be flexible so that the algorithms can be tested on different NoC designs. To this end, a scalab.e and flexible Network-on-Chip simulator is proposed. Some preliminary results obtained with this simnlator are presented, too. They show the flexibility of this simulator and that it is feasible for addressing the application mapping problem in a unified manner.
During the last years, especially due to the computing systems complexity growth, the need for tools which perform automatic design space exploration becomes more and more stringent. This paper presents a new initiate...
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ISBN:
(纸本)9781424473359
During the last years, especially due to the computing systems complexity growth, the need for tools which perform automatic design space exploration becomes more and more stringent. This paper presents a new initiated project having as the main aim developing a software tool, called FADSE (Framework for Automatic Design Space Exploration), that comes to meet this need. It is intended to provide out-of-the- box algorithms capable of solving single and multiobjective optimization problems. It focuses on automatic design space exploration for multicore and manycore systems. This tool is intended to be ftexible, to provide easy development and portability.
During the last years, especially due to the computing systems complexity growth, the need for tools which perform automatic design space exploration becomes more and more stringent. This paper presents a new initiate...
详细信息
ISBN:
(纸本)9781424473359
During the last years, especially due to the computing systems complexity growth, the need for tools which perform automatic design space exploration becomes more and more stringent. This paper presents a new initiated project having as the main aim developing a software tool, called FADSE (Framework for Automatic Design Space Exploration), that comes to meet this need. It is intended to provide out-of-the-box algorithms capable of solving single and multiobjective optimization problems. It focuses on automatic design space exploration for multicore and manycore systems. This tool is intended to be flexible, to provide easy development and portability.
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